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Journal articles on the topic 'Software defined radio(RTL)'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Silva Cabral, Yngrid Keila, Paulo Ribeiro Lins Júnior, and Jerônimo Silva Rocha. "Proposta de arcabouço experimental para rede de sensoriamento espectral usando rádio definido por software." Revista Principia - Divulgação Científica e Tecnológica do IFPB 1, no. 44 (April 2, 2019): 88. http://dx.doi.org/10.18265/1517-03062015v1n44p88-99.

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<p>This paper presents an architecture proposal for a spectrum sensing network using software defined radios. The radios responsible for the sensing are implemented with SDR-RTL, a low-cost radio, capable of receiving signals from several frequency bands, such as those used in FM, DAB and DVB-T. Sensing functions are implemented using GNU Radio, the most commonly used free software for configuring software-defined radios installed in Raspberry Pi’s, which makes the sensing structure significantly compact and inexpensive when compared to other solutions. Experiments are performed to measure the probability of detection in relation to the signal noiseratio, as a metric of the efficiency of the system proposed in this work</p>

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Rahman, Md Habibur, and Md Mamunoor Islam. "A Practical Approach to Spectrum Analyzing Unit Using RTL-SDR." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 151–59. http://dx.doi.org/10.3329/rujse.v44i0.30400.

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In the present scenario, there has been an immense advancement in the field of wireless communication in this modern engineering world. Now-a-days Software Defined Radio (SDR) technology is an indisputable emerging technology and presents new challenges for communications engineers. The advancement of SDR system has made significant progress in recent years which makes it as a serious substitute of traditional hardware radio architectures where the mathematical procedures are obligatory to decode and process radio signals using analogue circuitry. Recently, computers have turned out to be powerful enough to do the required mathematical calculations using software. So aim of this paper is to demonstrate a RTL-SDR based spectrum analyzer which can be used proficiently as an alternative of existing hardware spectrum analyzer. This approach will lessen the complexity of analogue hardware system with the higher tractability of software based filtering and demodulation techniques. As RTL-SDR devices are quite cheap (Approximately 20$) and small sized, this system also offers cost effectiveness with provision of portability. An experimental study was conducted with suitable conditions to examine the feasibility and efficiency of the proposed system. The outcome of experimental result is thoroughly examined in this paper.

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Satya Narayana, P., M. N.V.S. Syam Kumar, A. Keerthi Kishan, and K. V.R.K. Suraj. "Design approach for wideband FM receiver using RTL-SDR and raspberry PI." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 9. http://dx.doi.org/10.14419/ijet.v7i2.31.13386.

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Software defined radio replaced majority of hardware modules like mixers, filters, modulators and demodulators etc., with Software blocks in the field of radio electronics and communication. In this some or all the functionalities are Configurable using this software implemented on technologies like FPGAs, DSPs etc. Owing to lack of ease in implementing and reconfiguring huge hardware modules, we move on to implement an adaptable communication system with the help of SDR, as it can be easily configured to work with wide range of frequencies. We find various SDR transceiver modules which can be interfaced with digital computer and aided with firmware like GNU radio, SDR shark, etc., allowing us to construct blocks with the help of built in components that decode and process the received data and produce required output. In requirement of implementing a cost-effective, compact sized and portable system, we use a processing unit providing enough computational power to perform signal processing tasks which is Raspberry pi. Here we are going to implement a low cost SDR communication system that capture, process and visualize the Wide Band Frequency signal.

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Stewart, Robert W., Louise Crockett, Dale Atkinson, Kenneth Barlee, David Crawford, Iain Chalmers, Mike Mclernon, and Ethem Sozer. "A low-cost desktop software defined radio design environment using MATLAB, simulink, and the RTL-SDR." IEEE Communications Magazine 53, no. 9 (September 2015): 64–71. http://dx.doi.org/10.1109/mcom.2015.7263347.

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5

Rahman, Aviv Yuniar, Mamba’us Sa’adah, and Istiadi. "Noise Reduction in RTL-SDR using Least Mean Square and Recursive Least Square." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 2 (April 19, 2020): 286–95. http://dx.doi.org/10.29207/resti.v4i2.1667.

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Noise reduction is an important process in a communication system, one of which is radio communication. In the process of broadcasting radio Frequency Modulation (FM) often encountered noise so that listeners find it difficult to understand the information provided. In the past, noise reduction used traditional filters that were only able to filter certain frequencies. However, for future technologies an adaptive filter is needed that can dynamically reduce noise effectively. Register Level-Software Defined Radio (RTL-SDR) can capture signals with a very wide frequency range but has a less clear sound quality. So it needs to be done noise reduction. In this study, two methods are used, namely Least Mean Square (LMS) and Recursive Least Square (RLS). The data used five radio stations in Malang. The results showed that the LMS algorithm is stable but has a slow convergence speed, whereas the RLS algorithm has poor stability but has a high convergence speed. From the test, it can be concluded that the performance of RLS is better than LMS for noise reduction in RTL-SDR. The best performance is the reduction of White Noise using RLS on the Oryza radio station with an Normalized Weight Differences (NWD) value of -13.93 dB.

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6

Sabur, Fatmawati, and Ucok Sinaga. "Rancang Bangun Trainer Spectrum Analyzer berbasis Raspberry Phyton dan Register Transfer Level - Software Defined Radio." AIRMAN: Jurnal Teknik dan Keselamatan Transportasi 3, no. 2 (December 28, 2020): 1–8. http://dx.doi.org/10.46509/ajtk.v3i2.161.

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Spectrum Analyzer merupakan perangkat yang dapat menganalisis atau menguji kondisi suatu sistem frekuensi dalam suatu jaringan komunikasi. Kondisi yang terjadi dilapangan adalah harga beli dari sebuah Spectrum Analyzer yang tinggi menyebabkan tidak semua pihak mampu memilikinya. Salah satu solusi untuk mengatasi hal tersebut adalah dengan membuat suatu hardware yang mampu melakukan unjuk kerja yang sama seperti Spectrum Analyzer tetapi dengan harga yang relatif lebih murah. Prototype Spectrum Analyzer yang akan diimplementasikan merupakan suatu perangkat yang mampu menampilkan spektrum suatu sinyal pada range frekuensi tertentu. Rekayasa ini dilaksanakan dari bulan Juli sampai dengan Oktober 2020 di Kampus Polteknik Penerbangan Makassar dan ujicoba alat dengan alat pembanding dilaksanakan di Otban Wilayah V makassar. Teknik atau metode yang digunakan dalam pengumpulan data yaitu metode pustaka dengan cara mengumpulkan beberapa data tertulis baik dari buku, literatur, dan tutorial-tutorial di internet, sebagai bahan referensi kemudian menganalisa solusi yang dapat diambil dalam penyelesaian masalah. Dari hasil pengujian yang dilakukan dengan penggunaan RTL-SDR pada Single Board Computer (SBC) Raspberry pi dapat menampilkan spectrum frequensi baik itu dilakukan secara tunggal ataupun diterapan pada jaringan sehingga dapat digunakan sebagai media pembelajaran praktik teknologi wireless atau pun materi lain yang melakukan pengukuran frekuensi. Dari sergi performa kinerja trainer spectrum analyzer dengan pemanfaatan raspberry pisebagai perangkat untuk mengolah sinyal radio/ wireless cukup baik pada pemanfaatan dengan mode CLI (command line Interface) namun relatif lambat jika digunakan pada desktop sebagai alat portable spectrum analyzer yang dapat digunakan sebagai media pembelajaran

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Sabur, Fatmawati, and Ucok Sinaga. "Design Trainer Analysis Spectrum Analyzer Based on Raspberry Python and Register Transfer Level - Software Defined Radio." Airman: Jurnal Teknik dan Keselamatan Transportasi 3, no. 2 (February 4, 2021): 1–8. http://dx.doi.org/10.46509/ajtkt.v3i2.69.

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communication network. However, the high purchase price of a Spectrum Analyzer means that not everyone can afford it. One solution to overcome this problem is to make a piece of hardware capable of performing the same performance as a Spectrum Analyzer but at a relatively cheaper price. Prototype Spectrum Analyzer to be implemented is a device capable of displaying the spectrum of a signal in a certain frequency range. This engineering was carried out from July to October 2020 at the Makassar Aviation Polytechnic Campus and testing tools with a comparison tool was carried out at Otban Region V Makassar. The technique or method used in data collection is the library method by collecting some written data from books, literature, and tutorials on the internet, as reference material and then analyzing solutions that can be taken in solving problems. From the results of tests carried out by using RTL-SDR on the Single Board Computer (SBC), Raspberry pi can display the frequency spectrum whether it is done singly or applied to the network so that it can be used as a learning medium for wireless technology practice or other materials that measure frequency From sergi, the performance of the trainer spectrum analyzer with the use of raspberry pi as a device for processing radio / wireless signals is quite good at utilization with CLI mode (command line interface) but is relatively slow when used on the desktop as a portable spectrum analyzer that can be used as a learning medium

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8

Bing, B. "Software-Defined Radio Basics." IEEE Distributed Systems Online 6, no. 10 (October 2005): 6. http://dx.doi.org/10.1109/mdso.2005.54.

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9

Tuttlebee, W. H. W. "Advances in software-defined radio." Electronics Systems and Software 1, no. 1 (February 1, 2003): 26–31. http://dx.doi.org/10.1049/ess:20030105.

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10

Iancu, Daniel, John Glossner, Mihai Sima, Peter Farkas, and Michael McGuire. "Software-Defined Radio and Broadcasting." International Journal of Digital Multimedia Broadcasting 2009 (2009): 1–2. http://dx.doi.org/10.1155/2009/698402.

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11

Cass, Stephen. "Software-defined radio, part II." IEEE Spectrum 50, no. 9 (September 2013): 24–25. http://dx.doi.org/10.1109/mspec.2013.6587181.

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12

Jondral, Friedrich K., Jens Elsner, and Michael Schwall. "Software Defined Radio—Guest Editorial." Journal of Signal Processing Systems 69, no. 1 (January 13, 2012): 1–3. http://dx.doi.org/10.1007/s11265-011-0651-5.

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13

Tuttlebee, Walter HW. "Advances in software defined radio." Annales Des Télécommunications 57, no. 5-6 (May 2002): 314–37. http://dx.doi.org/10.1007/bf02995167.

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14

Qing, Liu, Cao Kai, and Lai Ying-yong. "FPGA Software Architecture for Software Defined Radio." Procedia Engineering 29 (2012): 2133–39. http://dx.doi.org/10.1016/j.proeng.2012.01.275.

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15

Chu, James. ""Intelligent" Software-Defined Radio ( [Book/Software Reviews]." IEEE Microwave Magazine 17, no. 11 (November 2016): 82–98. http://dx.doi.org/10.1109/mmm.2016.2600950.

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16

Macedo, Daniel F., Dorgival Guedes, Luiz F. M. Vieira, Marcos A. M. Vieira, and Michele Nogueira. "Programmable Networks—From Software-Defined Radio to Software-Defined Networking." IEEE Communications Surveys & Tutorials 17, no. 2 (2015): 1102–25. http://dx.doi.org/10.1109/comst.2015.2402617.

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17

Nambissan, T. Jishnu, T. V. Nikhil, and V. Vinodkumar. "A VHF Radio for Software Defined Radio Applications." Procedia Technology 24 (2016): 820–26. http://dx.doi.org/10.1016/j.protcy.2016.05.109.

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18

Rani, Supriya. "Software Defined Radio in Radio Frequency Identification Applications." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1887–92. http://dx.doi.org/10.22214/ijraset.2021.36778.

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RFID is an important aspect of today's age because it boosts efficiency and convenience. It is used for a lot of applications that prevent thefts of automobiles and merchandise. In current times there have been continuous transitions from analog to digital systems where software is being used to define the waveforms and analog signal processing is being replaced with digital signal processing. In this paper, we have done a thorough literature survey and understood the working of how software-defined radio is implemented in radio frequency identification for a better BER performance.

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19

Tato, Anxo. "Software Defined Radio: A Brief Introduction." Proceedings 2, no. 18 (September 19, 2018): 1196. http://dx.doi.org/10.3390/proceedings2181196.

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In this short article the concept of Software Defined Radio (SDR) is introduced and compared with the traditional radio. Then, a research project of atlanTTic center which used this technology was briefly presented and lastly, we include a reference to some dissemination activities related with SDR to be developed shortly.

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20

Jararweh, Yaser, Mahmoud Al-Ayyoub, Ahmad Doulat, Ahmad Al Abed Al Aziz, Haythem A. Bany Salameh, and Abdallah A. Khreishah. "Software Defined Cognitive Radio Network Framework." International Journal of Grid and High Performance Computing 7, no. 1 (January 2015): 15–31. http://dx.doi.org/10.4018/ijghpc.2015010102.

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Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.

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21

Frank, Thomas. "SICHERE HOCHFREQUENZFUNKTIONEN DANK SOFTWARE DEFINED RADIO." ATZelektronik 7, S7 (October 18, 2012): 54–57. http://dx.doi.org/10.1365/s35658-012-0214-y.

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22

Sherman, Jeff A., and Robert Jördens. "Oscillator metrology with software defined radio." Review of Scientific Instruments 87, no. 5 (May 2016): 054711. http://dx.doi.org/10.1063/1.4950898.

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23

Sadiku, M. N. O., and C. M. Akujuobi. "Software-defined radio: a brief overview." IEEE Potentials 23, no. 4 (October 2004): 14–15. http://dx.doi.org/10.1109/mp.2004.1343223.

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24

Birmingham, William, and Leah Acker. "Software-defined radio for undergraduate projects." ACM SIGCSE Bulletin 39, no. 1 (March 7, 2007): 293–97. http://dx.doi.org/10.1145/1227504.1227414.

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25

Sheybani, Ehsan, and Giti Javidi. "Integrating Software Defined Radio with USRP." International Journal of Interdisciplinary Telecommunications and Networking 9, no. 3 (July 2017): 1–9. http://dx.doi.org/10.4018/ijitn.2017070101.

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The USRP1 is the original Universal Software Radio Peripheral hardware (USRP) that provides entry-level RF processing capability. Its primary purpose is to provide flexible software defined radio development capability at a low price. You can control the frequency you receive and transmit by installing different daughter-boards. The authors' USRP model had been configured to receive a signal from local radio stations in the DC, Maryland metropolitan area with the BasicRX model daughterboard. The programmable USRP was running python block code implemented in the GNU Radio Companion (GRC) on Ubuntu OS. With proper parameters and sinks the authors were able to tune into the radio signal, record the signal and extract the in-phase (I) and quadrature phase (Q) data and plot the phase and magnitude of the signal. Using the terminal along with proper MATLAB and Octave code, they were able to read the I/Q data and look at the Fast Fourier Transform (FFT) plot along with the I/Q data. With the proper equations, you could determine not only the direction of arrival, but one would also be able to calculate the distance from the receiver to the exact location where the signal is being transmitted. The purpose of doing this experiment was to gain experience in signal processing and receive hands on experience with the USRP and potentially add a tracking system to the authors' model for further experiments.

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Yoon, Hyoseok, Ji-Eun Lee, Saet-Byeol Yu, and Se-Ho Park. "Bluetooth-enabled Software Defined Radio Platform." International Journal of Future Generation Communication and Networking 10, no. 7 (July 31, 2017): 1–12. http://dx.doi.org/10.14257/ijfgcn.2017.10.7.01.

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Li, Chunxiao, Niraj K. Jha, and Anand Raghunathan. "Secure reconfiguration of software-defined radio." ACM Transactions on Embedded Computing Systems 11, no. 1 (March 2012): 1–22. http://dx.doi.org/10.1145/2146417.2146427.

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28

Ulversoy, Tore. "Software Defined Radio: Challenges and Opportunities." IEEE Communications Surveys & Tutorials 12, no. 4 (2010): 531–50. http://dx.doi.org/10.1109/surv.2010.032910.00019.

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VULAVABETI, RAGHUNATH REDDY, and REDDY K. RAVINDRA. "SOFTWARE DEFINED RADIO BASED BEACON RECEIVER." i-manager's Journal on Communication Engineering and Systems 8, no. 3 (2019): 13. http://dx.doi.org/10.26634/jcs.8.3.16779.

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Cwalina, Krzysztof, Piotr Rajchowski, and Jarosław Sadowski. "Wideband Radio Direction Finder Implemented in Software Defined Radio Technology." Applied Mechanics and Materials 817 (January 2016): 348–55. http://dx.doi.org/10.4028/www.scientific.net/amm.817.348.

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In the paper a wideband radio direction finder (RDF) implemented in software defined radio (SDR) technology and the results of hardware layer research, including developed antenna switching unit (ASU), are presented. The results of tests of the devices, which are the part of the software defined radio platform (SDRP), and antenna switching unit, confirmed the possibility of using selected components in the final solution.

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31

Sârbu, Annamaria, and Dumitru Neagoie. "Wi-Fi Jamming Using Software Defined Radio." International conference KNOWLEDGE-BASED ORGANIZATION 26, no. 3 (June 1, 2020): 162–66. http://dx.doi.org/10.2478/kbo-2020-0132.

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AbstractIn this article we present software defined radio (SDR) instrumentation used for interfering or jamming Wi-Fi networks. A Wi-Fi network analyzer application was used together with a low cost, commercially available SDR, Hack RF one, to conduct aimed interference on a 802.11 b/g/n network. A GNU radio flowchart was used to control the radio transceiver (SDR) by emitting a jamming signal aimed towards the targeted client by means of a directional antenna. Various signal bandwidths and distance from the targeted device were tested to characterize the adequate parameters of an effective jamming signal with respect to the calculated signal to noise ratio (SNR). Jamming efficiency was evaluated by means of a Wi-Fi connectivity speed test application installed on the targeted device, in order to measure connectivity degradation if complete jamming was not possible. Results presented suggest that Wi-Fi jamming is possible by means of SDR technology, providing insights on the methodology used and initial optimisation procedures in the test environment.

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32

Machado-Fernández, José Raúl. "Software Defined Radio: Basic Principles and Applications." REVISTA FACULTAD DE INGENIERÍA 24, no. 38 (December 28, 2014): 79. http://dx.doi.org/10.19053/01211129.3160.

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<p align="justify">The author makes a review of the SDR (Software Defined Radio) technology, including hardware schemes and application fields. A low performance device is presented and several tests are executed with it using free software. With the acquired experience, SDR employment opportunities are identified for low-cost solutions that can solve significant problems. In addition, a list of the most important frameworks related to the technology developed in the last years is offered, recommending the use of three of them.</p>

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33

Mamidi, Suman, Emily Blem, Michael J. Schulte, John Glossner, Daniel Iancu, Andrei Iancu, Mayan Moudgill, and Sanjay Jinturkar. "Instruction set extensions for software defined radio." Microprocessors and Microsystems 33, no. 4 (June 2009): 260–72. http://dx.doi.org/10.1016/j.micpro.2009.02.005.

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34

Rabieirad, L., and S. Mohammadi. "Reconfigurable CMOS Tuners for Software-Defined Radio." IEEE Transactions on Microwave Theory and Techniques 57, no. 11 (November 2009): 2768–74. http://dx.doi.org/10.1109/tmtt.2009.2032464.

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35

Bertrand, J., J. W. Cruz, B. Majkrzak, and T. Rossano. "CORBA delays in a software-defined radio." IEEE Communications Magazine 40, no. 2 (2002): 152–55. http://dx.doi.org/10.1109/35.983922.

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Bagheri, R., A. Mirzaei, M. E. Heidari, S. Chehrazi, Minjae Lee, M. Mikhemar, W. K. Tang, and A. A. Abidi. "Software-defined radio receiver: dream to reality." IEEE Communications Magazine 44, no. 8 (August 2006): 111–18. http://dx.doi.org/10.1109/mcom.2006.1678118.

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37

Gomez, Ismael, Vuk Marojevic, and Antoni Gelonch. "Resource Management for Software-Defined Radio Clouds." IEEE Micro 32, no. 1 (January 2012): 44–53. http://dx.doi.org/10.1109/mm.2011.81.

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Garcia Reis, Andre Luiz, Andre Felipe Barros, Karlo Gusso Lenzi, Luis Geraldo Pedroso Meloni, and Silvio Ernesto Barbin. "Introduction to the Software-defined Radio Approach." IEEE Latin America Transactions 10, no. 1 (January 2012): 1156–61. http://dx.doi.org/10.1109/tla.2012.6142453.

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39

Cass, Stephen. "A $40 software-defined radio [Resources_Hands On]." IEEE Spectrum 50, no. 7 (July 2013): 22–23. http://dx.doi.org/10.1109/mspec.2013.6545114.

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40

Quan, Daying, Weitai Kong, and Xiaoping Jin. "MIMO Transceiver based on Software Defined Radio." IOP Conference Series: Earth and Environmental Science 234 (March 8, 2019): 012095. http://dx.doi.org/10.1088/1755-1315/234/1/012095.

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41

Gunawardena, Sanjeev, Thomas Pany, and James Curran. "ION GNSS software‐defined radio metadata standard." NAVIGATION 68, no. 1 (January 15, 2021): 11–20. http://dx.doi.org/10.1002/navi.407.

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Magnuski, Mirosław, Maciej Surma, and Dariusz Wójcik. "Broadband Input Block of Radio Receiver for Software-Defined Radio Devices." International Journal of Electronics and Telecommunications 60, no. 3 (October 28, 2014): 233–38. http://dx.doi.org/10.2478/eletel-2014-0029.

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Abstract In the paper a cost-effective input block of the SDR receiver for 0.9 — 2.4 GHz frequency band built of capacitive-tuned selective amplifier and broadband Vivaldi antenna is presented. The applied selective amplifier consists of three identical sections of tunable filters and two stages of monolithic broadband amplifiers. The single filter section proposed by the authors, due to its ability to absorb parasitic inductances of varicap diodes, simplifies usage of encapsulated varicap diodes in design of tunable in broad band selective filters dedicated to input stages of the receivers. Moreover, proposed filter section has small variation of in-band insertion loss in comparison to varicap-tuned filters built of coupled transmission lines which are commonly applied in input blocks of the microwave receivers. The described selective amplifier could be easily integrated on a single substrate with the Vivaldi antenna which is a cost effective way of fabrication of the tunable in broad band input block of a receiver that has desired gain, selectivity and directivity of the antenna.

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43

Suzuki, Hirotaka, Haruhisa Ichikawa, Jin Mitsugi, and Yuusuke Kawakita. "GNU Radio-based Cloud Development Environment for Software-defined Radio Users." Journal of Information Processing 27 (2019): 287–96. http://dx.doi.org/10.2197/ipsjjip.27.287.

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44

Arslan, Hüseyin, and Joseph Mitola. "Special Issue: Cognitive radio, software-defined radio, and adaptive wireless systems." Wireless Communications and Mobile Computing 7, no. 9 (2007): 1033–35. http://dx.doi.org/10.1002/wcm.478.

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Zhou, Ruolin, Omer Mian, Xue Li, Bin Wang, and Zhiqiang Wu. "A software-defined radio based cognitive radio demonstration over FM band." Wireless Communications and Mobile Computing 10, no. 1 (January 2010): 4–15. http://dx.doi.org/10.1002/wcm.903.

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46

Muzammil, Rehan, M. Salim Beg, and Mohsin M. Jamali. "A Dynamically Reconfigurable Transceiver for Software Defined Radio." International Journal of Computer Applications 76, no. 17 (August 23, 2013): 50–58. http://dx.doi.org/10.5120/13344-0716.

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47

Kun Hua, and Yin Wang. "Adaptive Software Defined Radio Detection with Artificial Intelligence." International Journal of Networked Computing and Advanced Information Management 1, no. 1 (October 31, 2011): 1–8. http://dx.doi.org/10.4156/ijncm.vol1.issue1.1.

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48

Estrela, Vania V. "Why Software-Defined Radio (SDR) Matters in Healthcare?" Medical Technologies Journal 3, no. 3 (November 11, 2019): 421–29. http://dx.doi.org/10.26415/2572-004x-vol3iss3p421-429.

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Vasudevan, A. Shriram K., B. Sivaraman R C., and Z. C. Alex. "Software Defined Radio Implementation (With simulation & analysis)." International Journal of Computer Applications 4, no. 8 (August 10, 2010): 21–27. http://dx.doi.org/10.5120/848-1183.

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Alam, Hasin. "AUTOMATED MODULATION CLASSIFICATION SYSTEM FOR SOFTWARE DEFINED RADIO." International Journal of Advances in Signal and Image Sciences 4, no. 2 (December 28, 2018): 1. http://dx.doi.org/10.29284/ijasis.4.2.2018.1-7.

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